In this work, we consider the complex control problem of making a monopod reach a target with a jump. The monopod can jump in any direction and the terrain underneath its foot can be uneven. This is a template of a much larger class of problems, which are extremely challenging and computationally expensive to solve using standard optimisation-based techniques. Reinforcement Learning (RL) could be an interesting alternative, but the application of an end-to-end approach in which the controller must learn everything from scratch, is impractical. The solution advocated in this paper is to guide the learning process within an RL framework by injecting physical knowledge. This expedient brings to widespread benefits, such as a drastic reduction of the learning time, and the ability to learn and compensate for possible errors in the low-level controller executing the motion. We demonstrate the advantage of our approach with respect to both optimization-based and end-to-end RL approaches.

翻译：本文研究了一个复杂的控制问题：使单足机器人通过跳跃到达目标位置。该机器人可以向任意方向跳跃，且其足部下方地形可能不平坦。此问题是一大类极具挑战性、且使用标准优化技术计算成本极高的典型问题。强化学习（RL）可能是一种有趣的替代方案，但采用让控制器从零开始学习一切的端到端方法并不实际。本文提出的解决方案是在RL框架内通过注入物理知识来引导学习过程。这一策略带来了广泛益处，例如大幅缩短学习时间，以及能够学习并补偿执行动作的低层控制器的潜在误差。我们展示了本方法相对于基于优化的方法和端到端RL方法的优势。